Yubo Yang
Hofstra University, NY·Physics
Modern computing rests on understanding how electrons behave in materials. A new class of materials may extend that legacy.
- Fields
Physics
Materials Science
Computer Science
- Topics
moiré materials
two-dimensional materials
2D materials
- Grants
- 1 known grant, through Sep 2027
What they work on
- Discover and characterize strongly correlated electronic phases in two-dimensional moiré materials, such as generalized Wigner crystals, using many-body wavefunction methods that go beyond mean-field theory.
- Use machine learning and neural quantum states to build more flexible variational ansätze, the trial wavefunctions that approximate how electrons arrange themselves.
- Run high-accuracy diffusion Monte Carlo simulations alongside density functional theory to separate real physical phases from artifacts of overfitting and to generate benchmark datasets.
More about the research
Moiré materials form when two-dimensional (atom-thin) layers are stacked with a slight twist or mismatch, creating a large repeating pattern. These materials could one day underpin energy-efficient spintronic devices, which use the electron's spin to carry information, and even fault-tolerant quantum computers. In moiré materials, electrons interact strongly with one another, producing unusual states that simple theories cannot describe accurately.
Why it matters
Accurate theoretical tools for moiré materials could help researchers find and understand new electronic phases. This could guide the design of future spintronic and quantum computing devices. The project's benchmark datasets will give other researchers reliable reference points for testing faster approximate methods.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
You Zhou
University of Maryland, MD·Physics·Early-career
Fabricate coupled bilayers of transition metal dichalcogenide semiconductors and test how their atomic structure and stacking affect the stability of Wigner crystals, ordered patterns of electrons.
Cyrus Dreyer
Stony Brook University, NY·Physics·Early-career
Develop quantum embedding methods that combine density-functional theory with many-body methods, improving how they treat electron repulsion in defect orbitals, defect–crystal mixing, and double-counting errors.
Yubo Qi
University of Alabama at Birmingham, AL·Physics
Develop a deep-learning-based, multifunctional computational method that reproduces density functional theory predictions of electron behavior while scaling to systems far larger than direct calculations allow.
The school
Hofstra University: 2 labs on PI Pages, $10M a year on research, 95 research doctorates a year.
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